Personalized capnography

ABSTRACT

Control logic, device and method including same configured to receive a measured carbon dioxide (CO2) related parameter of a patient, to obtain a patient specific baseline for said CO2 related parameter, the patient specific baseline determined based on a characteristic of the patient; to compute a deviation of the measured CO2 related parameter from the patient specific baseline; and to trigger an alarm when the deviation crosses a predetermined threshold value.

TECHNICAL FIELD

The present disclosure relates generally to the field of personalizedcapnography and alarm management.

BACKGROUND

Medical monitoring devices provide crucial data regarding a patient'smedical condition. For example capnographs measure and provides valuesof the carbon dioxide (CO₂) concentration in exhaled breath, and as suchmay be used to characterize patient's ventilation functioning.

The medical devices are often configured to trigger an alarm alertinghealth care providers that a monitored parameter deviates from athreshold value. For example, a capnograph may set off an alarm whendeviations or changes in the patient's CO₂ levels are detected.

SUMMARY

Aspects of the disclosure, in some embodiments thereof, relate topersonalized configured to interpret CO₂ waveforms according to apatient's personal data and medical history. The personalizedcapnography disclosed herein may facilitate the clinicians to swiftlyobtain a refined assessment of a patient's respiratory status.Furthermore, the personalized capnography may also be utilized to reducethe number of false alarms.

Frequent non-actionable alarms are a common complaint of caregivers.These alarms disrupt clinical workflow, are troubling to the patient andhis or her surroundings, and may lead to alarm fatigue amongst themedical personnel. As a consequence thereof, true alerts may beoverseen, as the alarm is ignored or even turned off, with a possiblytragic outcome.

According to some embodiments, the control logic, disclosed herein, isconfigured to obtain a measured carbon dioxide (CO₂) relatedparameter(s) of a patient, to obtain a patient specific baseline for theCO₂ related parameter(s), the patient specific baseline determined basedon a characteristic(s) of the patient, and to compute a deviation of themeasured CO₂ related parameter(s) from the patient specific baselineCO₂.

This may enable to trigger an alarm only when the measured CO₂ relatedparameter(s) deviates from a “personal” baseline rather than an“absolute” baseline common to all patients. Thus, the control logic,disclosed herein enables reducing the amount of both false positive andfalse negative alarms by personalizing the alarm settings. This may inturn avoid disruption of clinicians' workflow while enhancing theconfidence in the remaining alarms, consequently reducing the risk ofclinicians discounting true alert.

According to some embodiments, the control logic, disclosed herein, isconfigured to receive a measured CO₂ related parameter(s), to obtain apatient specific first baseline for the CO₂ related parameter, thepatient specific first baseline determined based on a backgroundvariable of the patient, and to obtain a patient specific secondbaseline for the CO₂ related parameter, the patient specific secondbaseline determined based on the background variable and on a backgrounddisease of the patient. The control logic is further configured tocompute a first deviation value based on a deviation of the measured CO₂related parameter from the first baseline and to compute a seconddeviation value based on a deviation of the measured CO₂ relatedparameter from the second baseline.

The control logic may thereby enable the caregiver to rapidly assess towhat extent measurements obtained from a patient are anomalous relativeto normal subjects sharing the same background variable(s) and relativeto subjects sharing the same background variable(s) as well as the samebackground disease(s).

According to some embodiments, there is provided a control logicconfigured to receive a measured carbon dioxide (CO₂) related parameterof a patient, obtain a patient specific baseline for the CO₂ relatedparameter, the patient specific baseline determined based on acharacteristic of the patient; compute a deviation of the measured CO₂related parameter from the patient specific baseline; and trigger analarm When the deviation crosses a predetermined threshold value.

According to some embodiments, the CO₂ related parameter may include endtidal CO₂ (EtCO₂), respiration rate, waveform shape, waveform scale orany combination thereof. Each possibility is a separate embodiment.

According to some embodiments, the patient characteristic may includeage, sex, weight, fitness, background disease or any combinationthereof. Each possibility is a separate embodiment.

According to some embodiments, the background disease may includeasthma, chronic obstructive pulmonary disease (COPD), broncho-pulmonarydysplasia (BPD) or any combination thereof. Each possibility is aseparate embodiment.

According to some embodiments, the CO₂ related parameter may include analgorithmically-derived index of multiple CO₂ related parameters.According to some embodiments, the algorithmically-derived index ofmultiple CO₂ related parameters is computed by:

(a) characterizing a first measured CO₂ related parameter based on acomparison of the first measured CO₂ related parameter against a firstreference value;

(b) characterizing a second measured CO₂ related parameter based on acomparison of the second measured CO₂ related parameter against a secondreference value; and

(c) computing the index value based on values associated with each ofthe characterized first and second measured CO₂ related parameters.

According to some embodiments, the patient specific baseline is a CO₂related parameter obtained from said patient prior to a medicalprocedure. According to some embodiments, the patient specific baselineis a CO₂ related parameter representative of patients having the samepatient characteristic. According to some embodiments, therepresentative CO₂ related parameter is provided to said control logicthrough a user interphase.

According to some embodiments, the control logic may be configured toup-load the patient specific baseline carbon dioxide (CO₂) relatedparameter and the measured carbon dioxide (CO₂) related parameter to aremote database. According to some embodiments, the database may becontinuously up-loaded. According to some embodiments, the database maybe configured to compute an integrated data-set (e.g. an integratedpatient specific baseline carbon dioxide (CO₂) related parameter)specific to patients having the same background characteristics (e.g.suffering from a particular background disease).

According to some embodiments, there is provided a control logicconfigured to receive a measured CO₂ related parameter from a patient;to obtain a patient specific first baseline for the CO₂ relatedparameter, the patient specific first baseline determined based on abackground variable of the patient; to obtain a patient specific secondbaseline for the CO₂ related parameter, the patient specific secondbaseline determined based on the background variable and on a backgrounddisease of the patient; to compute a first deviation value based on adeviation of the measured CO₂ related parameter from the first baseline;and to compute a second deviation value based on a deviation of themeasured CO₂ related parameter from the second baseline.

According to some embodiments, the control logic may be configured todisplay the first and second deviation values.

According to some embodiments, the control logic may be configured totrigger an alarm when the second deviation crosses a predeterminedthreshold value. According to some embodiments, the control logic may beconfigured to trigger an alert when the first deviation crosses apredetermined threshold value.

According to some embodiments, the CO₂ related parameter may include endtidal CO₂ (EtCO₂), respiration rate, waveform shape, waveform scale orany combination thereof. Each possibility is a separate embodiment.

According to some embodiments, the background variable may include age,sex, weight, fitness or any combination thereof. Each possibility is aseparate embodiment.

According to some embodiments, the background disease may includeasthma, chronic obstructive pulmonary disease (COPD), broncho-pulmonarydysplasia (BPD) or any combination thereof. Each possibility is aseparate embodiment.

According to some embodiments, the CO₂ related parameter may include analgorithmically-derived index of multiple CO₂ related parameters.According to some embodiments, the algorithmically-derived index ofmultiple CO₂ related parameters is computed by:

(a) characterizing a first measured CO₂ related parameter based on acomparison of the first measured CO₂ related parameter against a firstreference value;

(b) characterizing a second measured CO₂ related parameter based on acomparison of the second measured CO₂ related parameter against a secondreference value; and

(c) computing the index value based on values associated with each ofthe characterized first and second measured CO₂ related parameters.

According to some embodiments, the control logic may be configured toup-load the patient specific baseline carbon dioxide (CO₂) relatedparameter and the measured carbon dioxide (CO₂) related parameter to aremote database. According to some embodiments, the database may becontinuously up-loaded. According to some embodiments, the database maybe configured to compute an integrated data-set (e.g. an integratedpatient specific baseline carbon dioxide (CO₂) related parameter)specific to patients having the same background characteristics (e.g.suffering from a particular background disease).

Certain embodiments of the present disclosure may include some, all, ornone of the above advantages. One or more technical advantages may bereadily apparent to those skilled in the art from the figures,descriptions and claims included herein. Moreover, while specificadvantages have been enumerated above, various embodiments may includeall, some or none of the enumerated advantages.

BRIEF DESCRIPTION OF THE DRAWINGS

Some embodiments of the disclosure are described herein with referenceto the accompanying figures. The description, together with the figures,makes apparent to a person having ordinary skill in the art how someembodiments of the disclosure may be practiced. The figures are for thepurpose of illustrative discussion and no attempt is made to showstructural details of an embodiment in more detail than is necessary fora fundamental understanding of the teachings of the disclosure.

FIG. 1 is an illustrative flowchart of the operation of a control logic,according to some embodiments;

FIG. 2 is an illustrative flowchart of the operation of a control logic,according to some embodiments;

FIG. 3 is an illustrative flowchart of the operation of a control logic,according to some embodiments.

DETAILED DESCRIPTION

In the following description, various aspects of the disclosure will bedescribed. For the purpose of explanation, specific configurations anddetails are set forth in order to provide a thorough understanding ofthe different aspects of the disclosure. However, it will also beapparent to one skilled in the art that the disclosure may be practicedwithout specific details being presented herein. Furthermore, well-knownfeatures may be omitted or simplified in order not to obscure thedisclosure.

The present disclosure relates generally to the field of personalizedcapnography and alarm management.

There is provided, according to some embodiments, a control logicconfigured to receive a measured carbon dioxide (CO₂) related parameterof a patient, and to obtain a patient specific baseline for the CO₂related parameter, the patient specific baseline determined based on acharacteristic of the patient. The control logic may then compute adeviation of the measured CO₂ related parameter from the patientspecific baseline; and trigger an alarm if the deviation crosses apredetermined threshold value.

As referred to herein, the terms “patient” and “subject” mayinterchangeably be used and may relate to a subject being monitored by acapnograph or any other device configured to monitor CO₂ relatedparameters.

As used herein, the terms “clinician” and “caregiver” may beinterchangeably used and may refer to any medical personnel involved inthe care of the patient.

According to some embodiments, the terms “characteristic”, “variable”and “data” may be used interchangeably and may refer to any attribute ofthe subject which may influence CO₂ related parameter readings.According to some embodiments, the term “characteristic” may be abroader term and may include the term “variable”. According to someembodiments, patient variables may include, but are not limited to, age,sex, weight, fitness or any combination thereof. Each possibility is aseparate embodiment. According to some embodiments, patientcharacteristics may include, but are not limited to age, sex, weight,fitness, background disease or any combination thereof. Each possibilityis a separate embodiment. According to some embodiments exemplarybackground diseases include asthma, chronic obstructive pulmonarydisease (COPD), broncho-pulmonary dysplasia (BPD), hyperventilation,hypoventilation or any combination thereof. Each possibility is aseparate embodiment.

As used herein, the term “measured carbon dioxide (CO₂) relatedparameter” may refer to parameters obtained from capnograph readings.According to some embodiments, the parameters may be continuouslyobtained from the capnograph.

As used herein, the term “patient specific baseline” may refer to aparameter(s) serving as a reference point(s) to the measured CO₂ relatedparameters. According to some embodiments, the patient specific baselinemay refer to baseline parameters incorporating and/or taking intoconsideration the characteristics of the patient. According to someembodiments, the baseline CO₂ related parameter is a CO₂ relatedparameter obtained from the (same) patient for example prior to amedical procedure. Exemplary medical procedures, typically requiring CO₂monitoring, include sedation and surgery, but other procedures for whichcapnographic monitoring is recommended are also applicable. According tosome embodiments, the baseline CO₂ related parameter is a CO₂ relatedparameter obtained from patients having the same patientcharacteristic(s). According to some embodiments, the baseline CO₂related parameter may refer to data obtained from patients having thesame patient characteristic(s) and stored by a computer memory forexample, but not limited to, the control logic. According to someembodiments, the baseline CO₂ related parameter may refer to theoretical(textbook) data of waveforms representative of patients having the samepatient characteristic(s).

According to some embodiments, the control logic may be configured totransfer and/or upload the baseline CO₂ related parameter and/or themeasured carbon dioxide (CO₂) related parameter to a computer memory,for example, but not limited to a remote database or a centralizedsystem. According to some embodiments, the up-loaded data may then bedownloaded to calculate deviation values. According to some embodiments,the up-load may be continuous, semi-continuous, at predefined timepoints (e.g. every 5 min) or event oriented (e.g. every time analert/alarm is triggered). It is thus understood by one of ordinaryskill in the art that the database may be revised in an ongoing manner.According to some embodiments, a learning algorithm may be applied tothe stored data in order to further refine and/or personalize the dataset and in effect the parameters calculated therefrom.

According to some embodiments, the control logic may be furtherconfigured to produce an integrated data-set specific to patients havingthe same background characteristics e.g. suffering from a particularbackground disease), based on up-loaded baseline CO₂ related parametersand/or measured carbon dioxide (CO₂) related parameters from each of the“similar” patients.

According to some embodiments, the stored data may be configured tocapture the patient(s)'s response to a treatment (e.g. adjustment ofventilation machine settings), and thereby enable an improved andpersonalized evaluation of treatment efficiency. Moreover, the controllogic may be configured to produce a treatment recommendation, such as,but not limited to, deciding on ventilation settings based on theup-loaded personal data-set.

It is understood by one of ordinary skill in the art that since thepatient specific baseline differs among patients due to their differentpersonal characteristics, the control logic enables to personalize alarmsettings thereby reducing the amount of both false positive and falsenegative alarms.

According to some embodiments, the patient specific baseline may beretrieved by the control logic upon providing the patientcharacteristic(s) to the control logic. For example, the clinician mayprovide the patient characteristic(s) to the control logic through auser interface. According to some embodiments, the patientcharacteristic(s) may be encoded at the time of patient enrolment.According to some embodiments, the medical history including some orpart of the patient's characteristics may be retrieved from a medicalfile of the patient.

According to some embodiments, the CO₂ related parameter may include endtidal CO₂ (EtCO₂), respiration rate, waveform shape, waveform scale orcombinations thereof. Each possibility is a separate embodiment.

According to some embodiments “shape factors”, as used herein, maycharacterize and/or describe the shape or pattern of a CO₂ waveform. Ashape factor may include, for example, parameters of a non-linearfunction describing an upstroke of the waveform. The shape factors ofthe waveform are generally indicative of physiological condition(s) of apatient. For example, dominant shape factors of the waveform(s) mayrelate to respiratory processes such as the mechanics of breathing.Shape factors may be a parameter(s) of a function or a set(s) of binaryvalues (in the form of a vector or a matrix). It is understood to one ofordinary skill in the art, that different respiratory conditions mayinfluence the shape of the waveform, and thus the shape factors used todescribe the waveform. As a non-limiting example, the upstroke of theCO₂ waveform may be prolonged (slope decreased) in patients sufferingfrom respiratory disorders such as COPD.

According to some embodiments, “scale factors”, as used herein, may bethe waveform values and/or ratios, for example, height, width, width athalf-height, duty cycle, inhalation to exhalation ratio (I to E ratio)or any other value or combination of values. Scale factor featurestypically relate to general processes and/or body functions, such as,perfusion, shunt, metabolism, ventilation, respiration and the like. Itis understood to one of ordinary skill in the art, that differentrespiratory conditions may influence the scale factors used to describethe waveform. As a non-limiting example, the height of the CO₂ waveformmay be decreased (EtCO₂ decreased) in patients suffering fromrespiratory obstruction, such as for example asthma.

According to some embodiments, the term “a” may refer to at least one.According to some embodiments, the term “at least one” may refer to 1,2, 3, 4, 5, or more parameters. Each possibility is a separateembodiment. For example, with regards to CO₂ related parameters, the CO₂related parameters (measured and baseline) may be EtCO₂ and respirationrate (RR). Accordingly, the control logic may compute a delta EtCO₂value—the deviation between the measured EtCO₂ and the baseline EtCO₂;and a delta RR—the deviation between the measured RR and the baselineRR.

According to some embodiments, the control logic may compute thedeviation by simple subtraction of the measured CO₂ related parameter(e.g. measured RR) from the patient specific baseline (e.g. baselineRR).

According to some embodiments, the control logic may compute thedeviation by performing statistical analysis of the deviation over apredetermined period of time. It is understood by one of ordinary skillin the art that that monitoring devices, such as for examplecapnographs, may continuously monitor breath samples and thuscontinuously provide measurements of the CO₂ related parameter(s) to thecontrol logic. In effect, the deviation may be calculated based on astatistical analysis of n number of measurements obtained during apredetermined period of time for example y seconds. It is furtherunderstood that the measured CO₂ related parameter may be continuouslyupdated (moving average) such that each measured CO₂ related parameterprovided to the control logic may represent n number of measurementsobtained during a measurement window of y seconds and updated every zseconds. According to some embodiment, the measured CO₂ relatedparameter provided to the control logic may represent 0.5-100measurements. According to some embodiment, the measured CO₂ relatedparameter provided to the control logic may represent 2-50 measurements.According to some embodiment, the measured CO₂ related parameterprovided to the control logic may represent 5-25 measurements.

According to some embodiments, the CO₂ related parameter may include analgorithmically-derived index of multiple CO₂ related parameters.According to some embodiments, the algorithmically-derived index ofmultiple CO₂ related parameters may be computed by:

(a) characterizing a first measured CO₂ related parameter based on acomparison of the first measured CO₂ related parameter against a firstreference value;

(b) characterizing a second measured CO₂ related parameter based on acomparison of the second measured CO₂ related parameter against a secondreference value; and

(c) computing the index value based on values associated with each ofthe characterized first and second measured CO₂ related parameters.

As used herein, the term “alarm” may refer to an audible alarmconfigured to alert the clinician. According to some embodiments, theclinician may be required to approach the patient in order to turn thealarm off.

As used herein, the terms “alert” and “warning” may be interchangeablyused and may refer to a signal provided to a clinician, but which do notrequire his or hers immediate attention. According to some embodiments,the clinician may not be required to approach the patient in order toturn off the alert. As a non-limiting example, the alert may be anaudible signal provided to a clinician (for example through a personalcommunication device such as, but not limited to a beeper or a smartphone). According to some embodiments, the alert may be stored in themedical history of the patient for further use by caregivers.

According to some embodiments, the at least one CO₂ related parameter isuser selectable. It is understood by one of ordinary skill in the artthat different medical parameters may be measured for different medicalconditions.

According to some embodiments, the control logic is configured to storedata including, but not limited to, the at least one measured CO₂related parameter, the patient specific baseline, the deviation of theat least one measured CO₂ related parameter from the patient specificbaseline, alerts, alarms or any combination thereof. Each possibility isa separate embodiment.

According to some embodiments, the data, or parts thereof, may bereported to the clinician. According to some embodiments, the reporteddata may serve as a tool in the assessment of the patient's condition.

According to some embodiments, there is provided a control logicconfigured to: receive a measured CO₂ related parameter from a patient,to obtain a patient specific first baseline for the CO₂ relatedparameter, the patient specific first baseline determined based on abackground variable of said patient, and to compute a first deviationvalue based on a deviation of the measured CO₂ related parameter fromthe first baseline. The control logic may be further configured toobtain a patient specific second baseline for the CO₂ related parameter,the patient specific second baseline determined based on the backgroundvariable and on a background disease of the patient, and to compute asecond deviation value based on a deviation of the measured CO₂ relatedparameter from the second baseline.

According to some embodiments, the control logic may display the firstand second deviation values. This may enable the caregiver to rapidlyassess to What extent measurement obtained for a patient are anomalousrelative to normal subjects sharing the same background variable andrelative to subjects sharing the same background variable as well as thesame background disease.

According to some embodiments, the control logic may compare the firstdeviation value to a predetermined first threshold value and the seconddeviation to a predetermined second threshold value. It is understood byone of ordinary skill in the art the first threshold vale may be thesame or different from the second threshold value.

According to some embodiments, the control logic may trigger an alarmwhen the second deviation value crosses a (second) predeterminedthreshold value. It is understood by one of ordinary skill in the artthat this may serve to reduce the amount of actionable alarms as thealarm is triggered only when the measurements obtained are anomalous topatients suffering from the same background diseases and sharing thesame background variable. According to some embodiments, the controllogic may trigger an alert when the first deviation value crosses a(first) predetermined threshold value. As detailed above, the alert maynot require the clinician's immediate attention, but may provide anindication to the clinician that the measurements obtained from thepatient are abnormal as compared to healthy subjects having the samebackground variable. The alert may be an audible signal distinct fromthe traditional alarm in order to enable the clinician to distinguishbetween action requiring alarms and non-actionable alerts. According tosome embodiments, the alert may be stored in the medical history of thepatient (along with alarms) thereby assisting the clinician in assessingthe patients respiratory status.

It is understood that the deviation required to trigger the alarm may bethe same or different from the deviation required to trigger the alert.It is further understood that since the alarm settings have beenpersonalized, the deviations required to trigger the alarm may be lessthan the deviation required to trigger a traditional alarm.Advantageously, the more strict deviation requirements may not elevatethe number of triggered alarms, due to the personalized baselinesettings.

According to some embodiments, the CO₂ related parameter may include endtidal CO₂ (EtCO₂), respiration rate, waveform shape, waveform scale orany combination thereof. Each possibility is a separate embodiment.

According to some embodiments, the background variable may include age,weight, sex, fitness or any combination thereof. Each possibility is aseparate embodiment. It is understood by one of ordinary skill in theart that waveforms obtained from patients suffering from a backgrounddisease may deviate from normal waveforms in a manner dependent on thepatient's variable(s). As a non-limiting example, a normal weightpatient suffering from asthma may have a waveform closer to a ‘normal’waveform than a patient being both overweight and suffering from asthma.

According to some embodiments, the background diseases may includeasthma, chronic obstructive pulmonary disease (COPD), broncho-pulmonarydysplasia (BPD) or combinations thereof. According to some embodiments,the background disease may include hyper- and hypo ventilation.According to some embodiments, the background disease provided to thecontrol logic is ‘no background disease’.

According to some embodiments, the CO₂ related parameter may include analgorithmically-derived index of multiple CO₂ related parameters.According to some embodiments, the algorithmically-derived index ofmultiple CO₂ related parameters may be computed by:

(a) characterizing a first measured CO₂ related parameter based on acomparison of the first measured CO₂ related parameter against a firstreference value;

(b) characterizing a second measured CO₂ related parameter based on acomparison of the second measured CO₂ related parameter against a secondreference value; and

(c) computing the index value based on values associated with each ofthe characterized first and second measured CO₂ related parameters.

According to some embodiments, the first and/or second baselines may beretrieved by the control logic upon providing the background variableand the background disease to the control logic. Hence, the clinicianmay provide the patient background variable(s) and background disease(s)to the control logic for example through a user interface. According tosome embodiments, the background variable may be encoded at the time ofenrolling the patient. According to some embodiments, the medicalhistory and and/or background variable may be retrieved from a medicalfile of the patient.

According to some embodiments, there is provided a medical deviceincluding a control logic configured to receive a measured carbondioxide (CO₂) related parameter of a patient, and to obtain a patientspecific baseline for the CO₂ related parameter, the patient specificbaseline determined based on a characteristic of the patient. Thecontrol logic may then compute a deviation of the measured CO₂ relatedparameter from the patient specific baseline; and trigger an alarm ifthe deviation crosses a predetermined threshold value.

According to some embodiments, there is provided a medical deviceincluding a control logic configured receive a measured CO₂ relatedparameter from a patient, to obtain a patient specific first baselinefor the CO₂ related parameter, the patient specific first baselinedetermined based on a background variable of said patient, and tocompute a first deviation value based on a deviation of the measured CO₂related parameter from the first baseline. The control logic may befurther configured to obtain a patient specific second baseline for theCO₂ related parameter, the patient specific second baseline determinedbased on the background variable and on a background disease of thepatient, and to compute a second deviation value based on a deviation ofthe measured CO₂ related parameter from the second baseline.

According to some embodiments, the medical device may be a capnograph.

According to some embodiments, the medical device includes at least onesensor. According to some embodiments, the at least one sensor is a CO₂sensor, a flow sensor, an infra-red (IR) sensor or combinations thereof.According to some embodiments, the term “at least one” when referring toa sensor may include 1, 2, 3, 4, 5 or more sensors. Each possibility isa separate embodiment.

According to some embodiments, there is provided a method for reducingnon-actionable alarms. According to some embodiments the method mayinclude receiving a measured carbon dioxide (CO₂) related parameter of apatient, obtaining a patient specific baseline for the CO₂ relatedparameter, the patient specific baseline determined based on acharacteristic of the patient, computing a deviation of the measured CO₂related parameter from the patient specific baseline, and triggering analarm if the deviation crosses a predetermined threshold value

According to some embodiments, the method may also include determiningwhich medical parameters will be measured for example based on themedical record of the patient. According to some embodiments, the atleast one measured medical parameter may include end tidal CO₂ (EtCO₂),respiration rate, waveform shape, waveform scale or any combinationthereof. Each possibility is a separate embodiment.

According to some embodiments, the method may further include storingdata, such as the measured CO₂ related parameter, the patient specificbaseline, the deviation of the measured CO₂ related parameter from thepatient specific baseline, alerts, alarms or any combination thereof.Each possibility is a separate embodiment.

According to some embodiments, there is provided a method forpersonalizing capnography, the method including receiving a measured CO₂related parameter from a patient, obtaining a patient specific firstbaseline for the CO₂ related parameter, the patient specific firstbaseline determined based on a background variable of the patient, andto compute a first deviation value based on a deviation of the measuredCO₂ related parameter from the first baseline. The method may furtherinclude obtaining a patient specific second baseline for the CO₂ relatedparameter, the patient specific second baseline determined based on thebackground variable and on a background disease of the patient, and tocompute a second deviation value based on a deviation of the measuredCO₂ related parameter from the second baseline.

According to some embodiments, the method includes displaying the firstand/or second deviation values.

According to some embodiment, the method includes triggering an alarmwhen the second deviation value crosses a predetermined threshold value.According to some embodiments, the method includes triggering an alertwhen the first deviation value crosses a predetermined threshold value.

According to some embodiments, the method may include determining whichmedical parameters will be measured for example based on the medicalrecord of the patient. According to some embodiments, the at least onemeasured medical parameter may include end tidal CO₂ (EtCO₂),respiration rate, waveform shape, waveform scale or any combinationthereof. Each possibility is a separate embodiment.

According to some embodiments, the method may include storing data, suchas the measured CO₂ related parameter, the patient specific baseline,the deviation of the measured CO₂ related parameter from the patientspecific baseline, alerts, alarms or any combination thereof. Eachpossibility is a separate embodiment.

Before explaining at least one embodiment in detail, it is to beunderstood that aspects of the embodiments are not necessarily limitedin their application to the details of construction and the arrangementof the components and/or methods set forth herein. Some embodiments maybe practiced or carried out in various ways. The phraseology andterminology employed herein are for descriptive purpose and should notbe regarded as limiting.

Reference is now made to FIG. 1 which is an illustrative flowchart ofthe operation of a control logic, according to some embodiments. At step100, the control logic receives a measured carbon dioxide (CO₂) relatedparameter(s) of a patient having certain patient characteristic(s). Atstep 110, the control logic obtains a patient specific baseline for theCO₂ related parameter, the patient specific baseline determined based onthe same patient characteristic(s). At step 120, the control logiccomputes a deviation of the measured CO₂ related parameter(s) from apatient specific baseline. Does the deviation cross a predeterminedthreshold value, the control logic triggers an alarm, in step 130 b.Otherwise, the control logic returns to step 100 if the threshold valuehas not been crossed, as described in step 130 a.

Reference is now made to FIG. 2 which is an illustrative flowchart ofthe operation of a control logic, according to some embodiments. At step200, the control logic receives a measured carbon dioxide (CO₂) relatedparameter(s) of a patient determined as having certain backgroundvariable(s) and optionally background disease(s). At step 210 a, thecontrol logic obtains a patient specific first baseline for the CO₂related parameter, the patient specific first baseline determined basedon the (same) background variable(s) of the patient. At step 210 b, thecontrol logic obtains a patient specific second baseline for the CO₂related parameter; the patient specific second baseline determined basedon the (same) characteristic(s) and background disease(s) of thepatient. At step 220 a and 220 b, the control logic computes a first anda second deviation value, respectively, based on a deviation of themeasured CO₂ related parameter(s) from the obtained first and secondbaselines.

Reference is now made to FIG, 3 which is an illustrative flowchart ofthe operation of a control logic, according to some embodiments. At step300, the control logic receives a measured carbon dioxide (CO₂) relatedparameter(s) of a patient determined as having certain backgroundvariable(s) and optionally background disease(s). At step 310 a, thecontrol logic obtains a patient specific first baseline for the CO₂related parameter, the patient specific first baseline determined basedon the (same) background variable(s) of the patient. At step 310 b, thecontrol logic obtains a patient specific second baseline for the CO₂related parameter, the patient specific second baseline determined basedon the (same) characteristic(s) and background disease(s) of thepatient. At step 320 a, the control logic computes a first deviationvalue based on a deviation of the measured CO₂ related parameter(s) fromthe obtained first baseline. Has the first threshold value been crossed,the control logic triggers an alert, as in step 330 b. Otherwise, if thefirst threshold value has not been crossed, the control logic returns tostep 300, as described in step 330 a. Similarly, at step 320 b, thecontrol logic computes a second deviation value based on a deviation ofthe measured CO₂ related parameter(s) from the obtained second baseline.Has the second threshold value been crossed, the control logic triggersan alarm, as described in step 330 c. It is understood to one ofordinary skill in the art, that steps 320 a, and 320 b (and subsequentsteps 330 a/b and 330 c) may be performed simultaneously. Alternatively,step 320 a may be performed in a sequential manner (prior to or after)steps 320 b.

It is further understood that variations may occur in the operation ofthe control logic and that numerous cycles of operation is inherent tothe operation of the logic although a single operation cycle is alsooptional.

The terminology used herein is for the purpose of describing particularembodiments only and is not intended to be limiting. As used herein, thesingular forms “a”, “an” and “the” are intended to include the pluralforms as well, unless the context clearly indicates otherwise. It willbe further understood that the terms “comprises” or “comprising”, whenused in this specification, specify the presence of stated features,integers, steps, operations, elements, or components, but do notpreclude or rule out the presence or addition of one or more otherfeatures, integers, steps, operations, elements, components, or groupsthereof.

While a number of exemplary aspects and embodiments have been discussedabove, those of skill in the art will recognize certain modifications,additions and sub-combinations thereof. It is therefore intended thatthe following appended claims and claims hereafter introduced beinterpreted to include all such modifications, additions andsub-combinations as are within their true spirit and scope.

1-20. (canceled)
 21. A method for personalized patient monitoring,comprising: measuring a CO₂ related parameter of a patient using a CO₂sensor of a capnograph positioned on the patient; and using a processorto: determine a first patient specific baseline for the CO₂ relatedparameter that is based on a characteristic of the patient; determine asecond patient specific baseline for the CO₂ related parameter that isbased on a health condition of the patient; determine a first deviationof the CO₂ related parameter based on a relationship between the firstpatient specific baseline and the CO₂ related parameter; determine asecond deviation of the CO₂ related parameter based on a relationshipbetween the second patient specific baseline and the CO₂ relatedparameter; determine a respiratory condition of the patient based on thefirst deviation and the second deviation; output an alarm associatedwith the respiratory condition of the patient in response to the seconddeviation being outside a first predetermined threshold value; andoutput an alert associated with an abnormal CO₂ related parameter inresponse to the first deviation being outside a second predeterminedthreshold value.
 22. The method of claim 21, comprising using theprocessor to output a treatment recommendation based on the CO₂ relatedparameter, the first patient specific baseline, the second patientspecific baseline, or any combination thereof.
 23. The method of claim22, wherein the treatment recommendation comprises an adjustment to aventilation setting.
 24. The method of claim 21, wherein the firstpatient specific baseline is a CO₂ related parameter obtained from thesame patient.
 25. The method of claim 21, wherein the patientcharacteristic comprises age, sex, weight, fitness, and combinationsthereof.
 26. The method of claim 21, wherein the health conditioncomprises asthma, chronic obstructive pulmonary disease (COPD), bronchopulmonary dysplasia (BPD), or any combination thereof.
 27. The method ofclaim 21, comprising using the processor to instruct transfer of thepatient specific baseline, the CO₂ related parameter, or both, to aremote database.
 28. The method of claim 21, wherein the CO₂ relatedparameter comprises end tidal CO₂ (EtCO₂), respiration rate, waveformshape, waveform scale, an index of multiple CO₂ related parameters, orany combination thereof.
 29. The method of claim 21, comprisingdisplaying the CO₂ related parameter, the first deviation, the seconddeviation, or any combination thereof.
 30. A patient monitoring system,comprising: a capnograph comprising a CO₂ sensor configured to measure aCO₂ related parameter of a patient; and a processor configured to:determine a patient specific baseline for the CO₂ related parameterbased on a characteristic of the patient and a health condition of thepatient; determine a deviation of the CO₂ related parameter based on arelationship between the patient specific baseline and the CO₂ relatedparameter; determine a respiratory condition of the patient based on thedeviation; output an alarm associated with the respiratory condition ofthe patient in response to the deviation being outside a firstpredetermined threshold value; and output a treatment recommendation inresponse to the CO₂ related parameter, the patient specific baseline, orboth.
 31. The patient monitoring system of claim 30, wherein thetreatment recommendation comprises an adjustment to a ventilationsetting.
 32. The patient monitoring system of claim 30, wherein thepatient characteristic comprises age, sex, weight, fitness, or anycombination thereof.
 33. The patient monitoring system of claim 30,wherein the health condition comprises asthma, chronic obstructivepulmonary disease (COPD), broncho pulmonary dysplasia (BPD), or anycombination thereof.
 34. The patient monitoring system of claim 30,wherein the CO₂ related parameter comprises end tidal CO₂ (EtCO₂),respiration rate, waveform shape, waveform scale, an index of multipleCO₂ related parameters, or any combination thereof.
 35. The patientmonitoring system of claim 30, wherein the processor is configured todetermine an additional deviation of the CO₂ related parameter based ona relationship between an additional patient specific baseline and theCO₂ related parameter, and to output an alert associated with anabnormal CO₂ related parameter in response to the additional deviationbeing outside another predetermined threshold value.
 36. A method forpersonalized patient monitoring, comprising: using a processor to:receive a CO₂ related parameter as measured by a CO₂ sensor of acapnograph, wherein the CO₂ related parameter is an index of multipleCO₂ related parameters; determine a patient specific baseline for theCO₂ related parameter based on a characteristic and health condition ofthe patient; determine a deviation of the CO₂ related parameter based ona relationship between the patient specific baseline and the CO₂ relatedparameter; determine a respiratory condition of the patient based on thedeviation; and output an alarm associated with the respiratory conditionof the patient in response to the deviation being outside a firstpredetermined threshold value.
 37. The method of claim 36, comprisingusing the processor to determine an additional deviation of the CO₂related parameter based on a relationship between an additional patientspecific baseline and the CO₂ related parameter, and to output an alertassociated with an abnormal CO₂ related parameter in response to theadditional deviation being outside another predetermined thresholdvalue.
 38. The method of claim 37, comprising determining the index ofmultiple CO₂ related parameters by: comparing a first CO₂ relatedparameter to a first reference value to characterize the first CO₂related parameter; comparing a second CO₂ related parameter to a secondreference value to characterize the second CO₂ related parameter; andgenerating an index value based on values associated with eachcharacterized first and second CO₂ related parameter.
 39. The method ofclaim 37, wherein the health condition comprises asthma, chronicobstructive pulmonary disease (COPD), broncho pulmonary dysplasia (BPD),or any combination thereof.
 40. The method of claim 37, wherein the CO₂related parameter comprises end tidal CO₂ (EtCO₂), respiration rate,waveform shape, waveform scale, or any combination thereof.